ETNA : Embedding Transformation Network with Attention

This is our Pytorch implementation for the paper:

Raehyun Kim and Hyunjae Kim (2019). Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network. In Proceedings of SIAM International Conference on Data Mining (SDM'19)

The code is tested under a Linux desktop (w/ TiTan X - Pascal) with Pytorch 1.0.0. and Python 3.

MVP (Multi-Vendor loyalty Program) Dataset

We provide dataset for demographic prediction. You can find our raw dataset in (data/raw).

MVP dataset consists of three files. [Company_info, User_info, Purchase_history]

• company_info.csv : Company's industrial categories are included in company info.
• user_info.csv : User's demographic information (processed as class).
• purchase_history.json : Each user's purchasing history.

Model Training and Evaluation

We have two type of task settings. (New user and partial prediction)

And user should specify observation ratio for partial prediction task.

To train our model on partial task with 50% of observation ratio (with default hyper-parameters):

python main.py --model_type ETNA --task_type partial50 

Experiments on other observed ratios are also available as follows:

python main.py --model_type ETNA --task_type partial10 

If you want to test our model on validation set for searching your own hyper-parameters, use '--do-validation' argument.

Note that we do not provide validation set, so you should use some portion of the training set as validation set.

Reference

Please cite our paper if you use the code or datasets.

@inproceedings{kim2019predicting,
  title={Predicting multiple demographic attributes with task specific embedding transformation and attention network},
  author={Kim, Raehyun and Kim, Hyunjae and Lee, Janghyuk and Kang, Jaewoo},
  booktitle={Proceedings of the 2019 SIAM International Conference on Data Mining},
  pages={765--773},
  year={2019},
  organization={SIAM}
}